class_docstr: Standardizes the series by subtracting mean and diving by standard deviation.
code: def fit_transform(self, df: pd.DataFrame) -> pd.DataFrame:
      self.stats_ = (
          df.replace([np.inf, -np.inf], np.nan)
          .groupby(self.id_col)[self.target_col]
          .agg(["mean", "std"])
          .rename(columns={"mean": "_mean", "std": "_scale"})
      )
      self.stats_["_scale"] = self.stats_["_scale"].clip(
          lower=self.min_scale
      )
      df = df.merge(self.stats_, on=self.id_col)
      df[self.target_col] = (df[self.target_col] - df["_mean"]) / df[
          "_scale"
      ]
      df = df.drop(columns=["_mean", "_scale"])
      return df
  
file_path: autogluon/timeseries/src/autogluon/timeseries/models/autogluon_tabular/utils.py
imports: import numpy
  import pandas
  from mlforecast.target_transforms import BaseTargetTransform
label_desc: Use image node_id 1 for calling the StandardScaler obj's underlying member method code with example usage: obj.fit_transform(df) and returns: df